From periphery immunity to central domain through clinical interview as a new insight on schizophrenia

Identifying disease predictors through advanced statistical models enables the discovery of treatment targets for schizophrenia. In this study, a multifaceted clinical and laboratory analysis was conducted, incorporating magnetic resonance spectroscopy with immunology markers, psychiatric scores, and biochemical data, on a cohort of 45 patients diagnosed with schizophrenia and 51 healthy controls. The aim was to delineate predictive markers for diagnosing schizophrenia. A logistic regression model was used, as utilized to analyze the impact of multivariate variables on the prevalence of schizophrenia. Utilization of a stepwise algorithm yielded a final model, optimized using Akaike’s information criterion and a logit link function, which incorporated eight predictors (White Blood Cells, Reactive Lymphocytes, Red Blood Cells, Glucose, Insulin, Beck Depression score, Brain Taurine, Creatine and Phosphocreatine concentration). No single factor can reliably differentiate between healthy patients and those with schizophrenia. Therefore, it is valuable to simultaneously consider the values of multiple factors and classify patients using a multivariate model.

of predictive approaches that amalgamate genetic, biochemical, imaging, and clinical parameters.Integrating these diverse data through advanced statistical models paves the way for a more comprehensive assessment of the diagnostic and predictive potential of selected indicators in schizophrenia.Our study represents the first of its kind to integrate multifaceted factors into a predictive model for schizophrenia, setting a precedent for future research in this field.

Characteristics of the sample
The results from N = 96 subjects in the groups of healthy subjects (control) and patients with schizophrenia (test) were examined.The study encompassed the outcomes of sociodemographic data, laboratory parameters, clinical assessment parameters, and Magnetic Resonance Spectroscopy (MRS) parameters in two locations (anterior cingulate cortex and posterior cingulate cortex) for two Time to Echo (TE) values (front TE = 30 ms, front TE = 144 ms, rear TE = 30 ms, rear TE = 144 ms).
The comprehensive characteristics of the sociodemographic data for the study sample are presented in Supplementary Table S1, which indicates that there were no significant differences between the groups concerning gender and age.

Laboratory parameters
The distribution of laboratory parameters for the entire sample and by groups is reported in Supplementary Table S2.According to those findings, there were significant differences between the groups on 22 parameters.In the schizophrenia group, the following parameters were altered.

Clinical evaluation
The distribution of clinical evaluation scales for all participants in the entire sample and by groups is presented in Table 1 and Supplementary Table S3.
Our results reveal significant differences between the groups for all seven evaluated parameters, where the schizophrenia group was characterized as follows.
Significantly higher: • BDI-II (the Beck Depression Inventory) scores www.nature.com/scientificreports/ The medians of positive and negative symptoms were distributed almost equally among the test group (Supplementary Table S3).Results of brain imaging analyses are provided in the supplementary data mentioned in each section below, in which they are presented both for all participants in the entire sample as well as detailed by groups.

Anterior cingulate cortex (ACC) in TE 30 ms and in TE 144 ms
The distribution of metabolite values in Anterior cingulate cortex at TE 30 ms for all participants in the entire sample and by groups is presented in Supplementary Table S4.
We identified significant differences between the groups for 10 brain parameters, with the following regularities for the schizophrenia group.
Significantly elevated: • The ratio of Glucose to the sum of creatine and phosphocreatine (Cr + PCr) Significantly lower: • Creatine concentration (Creatine conc.) • Glutamine concentration (Glutamine conc.) • The ratio of glutamine concentration to the sum of creatine and phosphocreatine concentrations (Glutamine/ (Cr + PCr)) • Glutamate concentration (Glutamate conc.)• Inositol concentration (Inositol conc.) • N-Acetyl aspartate concentration (N-acetyl aspartate conc.) • THE sum of creatine and phosphocreatine concentrations (Cr + PCr conc.) • The sum of glutamate and glutamine concentrations (Glu + Gln conc.)• The ratio of the sum of glutamate and glutamine concentrations to the sum of creatine and phosphocreatine concentrations ((Glu + Gln)/(Cr + PCr)) The distribution of metabolite values in the Anterior cingulate cortex at TE 144 ms is presented in Supplementary Table S5 and indicates significant differences between the groups for 10 brain parameters.All those 10 parameters were significantly higher in the control group:  S4),the schizophrenia group differed significantly from the control group as follows.
Significantly higher values: • Taurine concentration • The ratio of Taurine to the sum of creatine and phosphocreatine (Cr + PCr) Significantly lower levels: • The sum of creatine and phosphocreatine (Cr + PCr) concentration • Lip20 concentration • The ratio of Lip20 to the sum of creatine and phosphocreatine (Cr + PCr) The results presented in Supplementary Table S5 present the distribution of metabolite values in the Posterior cingulate cortex at TE 144 ms and indicate significant differences between the groups across seven brain parameters.
The control group exhibited notably higher levels in: Conversely, the control group showed significantly lower levels in: • The ratio (Glycerophosphocholine + Phosphocholine)/(Cr + PCr) • The ratio of Creatine/(Cr + PCr).

Determination of the original regression model
Out of the 63 variables displaying significant univariate effects, 27 potential predictive candidate variables were chosen for inclusion in the primary model, based on literature data and own earlier studies, to elucidate the variation in the occurrence of schizophrenia.These selected variables are as follows: White Blood Cells (WBC www.nature.com/scientificreports/ In addition, possible confounding factors have been included in an additional modelling in order to verify their significance.Those variables included age, and sex.It turned out that those factors did not influence the final model.

Application of the stepwise algorithm
The fit of the original model was separately evaluated for both the logit and probit link functions using the AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) criteria.The AIC metric yielded the smallest criterion value for the logit link function, mainly due to its greater sensitivity to outliers when compared to a logistic sigmoidal curve.
Utilizing a stepwise algorithmic approach, the predictive model underwent refinement, where the initial set of 27 candidate variables was systematically evaluated and pruned down to the eight most predictive variables.This selection process was adjusted for patient age and sex to ensure that the resulting model accounted for these fundamental confounding factors.The effectiveness of the final model refinement was evidenced by a reduction in the AIC value, from an initial 76.5 down to 69.8.

Results of fitting the final model
As a consequence of the stepwise algorithm, the ultimate model, fitted through AIC using a logit linking function, encompassed eight predictors and was characterized by the following Eq.( 1): 1 Rear rim turn TE 30 ms.
The outcomes of the model that was fitted are displayed in Table 2.
The fitted model satisfied the assumptions for the logistic regression model concerning the collinearity parameter, normality of the residuals' distributions, and its ability to replicate the observed data.
The lines predicted by the model in the posterior predictive check closely approximated those of the observed data, providing evidence that the model accurately simulated the actual data.According to the data in Supplementary Table S7, the VIF parameters for all predictors were below 3.0, suggesting a low level of collinearity among the explanatory variables.
Ensuring that the assumptions of the regression model were met allowed us to confidently assert that the predictions, confidence intervals, and scientific observations obtained from the fitted model were not misleading or biased.
Figure 1 illustrates a model curve of the Receiver Operating Characteristic (ROC) based on the specificity and sensitivity parameters.
The area under the model fit curve was 94.7%, indicating excellent discrimination by the model.The results of the Hosmer-Lemeshow test, χ 2 (6) = 1.39, p = 0.966, the modified Hosmer-Lemeshow test, F(7) = 0.64, p = 0.724, and the Osius and Rojek test, z < 0.01, p = 1.000, all exceeded 5%, suggesting there was no significant difference between the observed data and the predicted values 28,29 .This supports the assumption that the model is a good fit.

Estimation of marginal effects
The outcomes of the marginal effects estimates for each predictor in the regression model are presented in Supplementary Table S6.

Estimation of cutoff points for each covariate of the final regression model
The estimated optimal cutpoints with the performance metrics for binary classification for each covariate from the final regression model are presented in Supplementary Table S7 and reveal that the classification metrics for patient groups are fairly average and predominantly consist of a relatively high number of false negatives based on individual predictors.
This led to the conclusion that there are no individual factors that can consistently discriminate between healthy patients and those with schizophrenia.Hence, it is valuable to collectively assess the values of all factors and differentiate patients based on a multivariate model.

Discussion
Consistent with our earlier findings and the outcomes of other researchers, systemic inflammation and immune mechanisms play a substantial role in the pathogenesis of schizophrenia 2,30 .
(1) ).This finding did not align with the widely recognized modest male predominance in schizophrenia incidence, suggesting that in this sample, sex may not be a determinant factor.Age revealed an inverse relationship with the likelihood of schizophrenia, with the OR of 0.91 indicating a 9% decrement in odds per year increase, approaching significance (p = 0.068).This trend underscored the heightened vulnerability among the younger population, resonating with the typical age of onset for schizophrenia during late adolescence to early adulthood.White blood cell (WBC) count, a proxy for inflammatory status, showed a non-significant elevation in odds (OR = 1.40, p = 0.202), a finding that was consistent with the hypothesis of a neuroinflammatory component in schizophrenia pathophysiology but lacked the statistical power to corroborate this role.The parameter of reactive lymphocytes (Re-lymph) was markedly elevated (OR = 3.74 × 10 20 , p = 0.004), suggesting a profound association with schizophrenia.However, the magnitude of the OR and the breadth of the confidence interval.The significant association between reactive lymphocytes and schizophrenia occurrence may reflect the role of immune system dysregulation in the pathogenesis of the disorder.Some hypotheses suggested that inflammatory processes may contribute to the development of schizophrenia, potentially through the disruption of neurodevelopmental processes or by altering neurotransmitter systems central to the disorder.Red blood cell (RBC) count was positively associated with schizophrenia occurrence (OR = 7.77, p = 0.143), though this was not statistically significant within this sample.While the link between RBCs and schizophrenia was not well-established, this could hint at peripheral biomarkers reflecting central nervous system (CNS) pathology.The association between RBC count and schizophrenia, could suggest a link to oxygen transport and by extension, brain metabolism.Abnormalities in RBC indices have been studied for their potential role in various psychiatric conditions, possibly reflecting broader physiological or developmental disturbances.Glucose levels were significantly associated with schizophrenia (OR = 25.02,p = 0.014), aligning with the literature on glucose dysregulation in schizophrenia, possibly linked to insulin resistance or disrupted glucoregulatory mechanisms.The strong association between elevated glucose levels and schizophrenia occurrence aligned with previous research indicating that metabolic syndrome was more prevalent among individuals with schizophrenia.This could be related to the disease itself, lifestyle factors, or the side effects of antipsychotic medications, many of which can induce glucose intolerance and weight gain.Insulin levels, while displaying an OR of 1.10 (p = 0.227), did not reach statistical significance, suggesting a less pronounced role in schizophrenia within this cohort, despite the established connection between antipsychotic treatment and insulin resistance.The Beck Depression Inventory II (BDI II) score, indicative of depressive symptomatology, emerged as a significant predictor (OR = 1.11, p = 0.007), reflecting the comorbidity and symptomatic overlap between depression and schizophrenia, with affective dysregulation being a key component of the schizophrenia spectrum.The ratio of taurine to creatine plus phosphocreatine (Cr + PCr) presented a subtle but significant increase in odds (OR = 1.03, p = 0.013), suggesting alterations in amino acid neurotransmitter levels and energy metabolism may play a role in schizophrenia pathology.Finally, the concentration of creatine plus phosphocreatine (Cr + PCr) itself was inversely associated with schizophrenia occurrence (OR = 0.90, p = 0.008), potentially reflecting disturbances in brain energy homeostasis and cellular energetics within the disorder.These findings necessitate replication in broader cohorts and warrant longitudinal studies to ascertain causality and the temporal dynamics of these associations.www.nature.com/scientificreports/While previous research primarily concentrated on measuring cytokines and neurotransmitter metabolites in blood or cerebrospinal fluid, this study adopts a comprehensive approach by considering a broad spectrum of laboratory, cerebral, and clinical parameters.It underscores the complexity of the clinical situation, evidencing that individual parameters may not adequately elucidate or predict outcomes.
Interpreting the eight-factor predictive model developed in this study, the elevated neutrophil and reactive lymphocyte counts may suggest a pathological brain process associated with blood-brain barrier impairment and central nervous system dysregulation, ultimately leading to brain dysfunction.It is also possible that parallel processes with bidirectional effects on the central nervous system and the periphery are being investigated.
Pavlović's study revealed significantly higher total white blood cell counts in individuals with schizophrenia compared to the control group.The literature on this topic suggests a link between the neutrophil-to-lymphocyte ratio (NLR) and elevated leukocyte, neutrophil, and monocyte counts in children and adolescents with schizophrenia 31,32 .
The total white blood cell and neutrophil counts show a positive correlation with glucose levels and a negative correlation with HDL cholesterol levels, as demonstrated by Pavlović et al. in their 2016 study 33 33,34 .
The robust positive associations noted in this study between glucose and insulin levels in schizophrenia patients can be independent risk factors for the onset of metabolic syndrome in individuals with schizophrenia 35 .
In a study by Torsvik et al., a positive correlation with triglyceride levels and a negative correlation with "good" HDL cholesterol was prevalent in both schizophrenia and bipolar disorder, resulting in the grouping of patients.Shared transcriptome signatures associated with lipid changes and clinical translational potential were detected, influencing altered innate immunity pathways and resulting in increased expression of the same genes among patients in the shared cluster 36 .
The conducted research revealed a significant increase in scores on the Beck Depression Inventory-II (BDI-II) among individuals with schizophrenia compared to the control group.This finding was particularly intriguing to us, considering our earlier results indicating the presence of phenotypically distinct subgroups of patients with schizophrenia.These subgroups are characterized by variations in glutaminergic transmission as the primary mechanism linking peripheral changes to the brain, ultimately contributing to the emergence of negative symptoms 11 .
Interestingly, there is a notable increase in gastrointestinal symptoms in healthy individuals when compared to those with schizophrenia.This phenomenon may be attributed to an altered perception of pain.Insensitivity to pain in schizophrenia is a multifaceted condition that, in addition to the factors mentioned previously, may also be related to an abundance of negative symptoms.These negative symptoms, prevalent in our group of individuals with schizophrenia, can significantly impact the way patients express their experience of pain 37 .The molecular basis for the observed alterations in pain perception in central nervous system (CNS) disorders like anxiety, schizophrenia, and depression can be attributed to nerve inflammation, a complex process that involves both the peripheral circulation and the central nervous system (CNS) 38 .This concept is reflected in the predictive model of the current study, which combines immunological, metabolic, and central parameters.In the conducted study, apart from the noted metabolic changes in astrocytes that are associated with a reduction www.nature.com/scientificreports/ in myo-inositol, there are also significant changes in the health and functioning of neurons, which are linked to a notable decrease in the concentration of N-acetyl aspartate when compared to the control group.This study offers evidence of disruptions in the activation of the hypothalamic-pituitary-adrenal (HPA) axis and abnormal regulatory mechanisms in schizophrenia by revealing increased levels of dehydroepiandrosterone sulfate (DHEA-S) in the blood of individuals with schizophrenia when compared to controls.These findings are in line with research conducted by Babinkostova et al., where patients with schizophrenia exhibited significantly higher levels of cortisol and DHEA-S in their blood in comparison to the control group 39 .DHEA-S, released from the adrenal glands and metabolized in the nervous system, is associated with neurosteroid synthesis and the modulation of brain metabolites, which were notably lower in our patient group in comparison to the controls 40 .Our results are consistent with those of Miodownik et al., who identified elevated levels of DHEA-S, cholesterol, and insulin in individuals with schizophrenia compared to controls 41 .
Our research indicates a diminished presence of energy substances such as creatine and phosphocreatine in the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) in individuals with schizophrenia.This reduced concentration of creatine/phosphocreatine is evident at two distinct echo times in both the anterior and posterior cingulate regions, closely associated with the production and utilization of ATP as an energy source in the brains of those affected by schizophrenia.Our observations align with the findings of Sarramea Crespo et al., who observed a decrease in the creatine/phosphocreatine cycle in the cingulate gyrus of patients with comorbid schizophrenia and bipolar affective disorder 42 .PCr/CK plays a critical role in maintaining a stable ATP level during fluctuations in energy demand and connecting ATP production with its utilization site 43 .Various studies have shown that CK activity in the frontal lobe is diminished in patients with chronic schizophrenia, first-episode psychosis (FEP), and the first episode of bipolar affective disorder (BD) with psychotic features 44 .These findings may help elucidate the 8-factor predictive model presented in our research, where the sum of Creatine and Phosphocreatine, Taurine/(Cr + PCr), Beck's Depression Scale, Glucose (together with Insulin), and Re-Lymph are identified as key predictive markers for schizophrenia.
Currently, the absence of objective biomarkers poses a challenge to diagnostic and therapeutic decisions in schizophrenia.Our study emphasizes the potential diagnostic significance of routine blood parameters (white blood cells, neutrophils, and reactive lymphocytes) and common biochemical markers (thyroxine, glucose, and uric acid) for predicting concurrent metabolic disorders, type 2 diabetes, insulin resistance, as well as thyroid and adrenal issues in individuals with schizophrenia.Furthermore, neuroimaging can provide quantifiable biomarkers that aid in comprehending molecular distinctions in brain circuits.
Highlighting the interplay among the immune system, brain function, peripheral metabolism, and schizophrenia, a comprehensive evaluation involving larger groups of patients has the potential to establish reliable predictors for this intricate disorder in clinical settings.Our research underscores the importance of a personalized, multifaceted model tailored to specific patient subgroups, utilizing quantifiable data from laboratory tests, imaging, clinical investigations, and mental health assessments.
To the best of our knowledge, this is the sole study conducted in humans where the occurrence of schizophrenia was determined by predictive modelling that encompasses laboratory, clinical, and brain-related factors.Supplementary Table S7 provides the estimated optimal cutoff points and the performance metrics for binary classification of each variable derived from the final regression model.The results we obtained confirm our prior findings, which suggested a connection between brain bioenergetic dysfunction and the initial symptoms of schizophrenia.This dysfunction impacts glucose metabolism, insulin resistance, and neuronal development 25 .Disturbances in glutamatergic neurotransmission, nerve inflammation, and redox dysregulation are notable features of individuals with schizophrenia endophenotypes, particularly those exhibiting negative symptoms 11 .
The incorporation of variables such as creatine, phosphocreatine, taurine, neutrophils, and reactive lymphocytes in the ultimate predictive model further underscores the significance of inflammation and oxidative stress in schizophrenia 2 .Alterations in neutrophil and lymphocyte levels signify an underlying pathological brain process resulting in dysfunction, thereby supporting the notion that nerve inflammation is a substantial etiological factor in schizophrenia 45 .
Changes in taurine levels have been observed in individuals with acute polymorphic psychosis and depression, which corresponds with our study where we incorporated the Beck Depression Inventory-II (BDI-II) alongside other predictive factors 46,47 .Wu et al. 's research elucidates the antidepressant impacts of taurine by enhancing the expression of brain-derived neurotrophic factor (BDNF), influencing the survival, proliferation, and differentiation of neural stem cells through the BDNF/ERK/CREB pathway 48 .
The reduced taurine levels in the anterior cingulate cortex (ACC) of individuals with schizophrenia could potentially account for the detected molecular distinctions in glutamine levels, especially in individuals with predominant negative or cognitive symptoms of schizophrenia, as indicated in our earlier investigations 48,49 .

Limitations
The identified limitations of the study are twofold and are related to the complexity of the data and the size of the participant group.
The study's data set is inherently complex and multivariate.Although basic demographic variables such as age and gender were included, the small sample size limited the ability to add and analyze a wider range of confounders.Important factors such as type and duration of medication and duration of illness-important for adjusting for confounding effects-were not included in the analysis due to the limited sample.
Furthermore, relying solely on cross-validation within the same sample may not account for all bias specific to a given dataset, potentially biasing the results.This limitation may have an impact on the degree of generalizability of the findings, as there is no data on how well the model will perform in different datasets.

Table 1 .
Baseline clinical characteristics of participants.a Mdn (Q1, Q3).b Wilcoxon rank sum test.*This information is based on data collected from 96 patients, comprising 45 healthy volunteers and 51 individuals diagnosed with schizophrenia, who underwent clinical examinations and various procedures at the University Hospital in Kraków, Poland.The assessment tools used included GAF (Global Assessment of Functioning), BDI-II (the Beck Depression Inventory), STAI (the State and Trait Anxiety Inventory), GRSR (Gastrointestinal Symptom Rating Scale), GQH 28 (General Health Questionnaire-28), TEC (the Traumatic Experiences Checklist), ECR-RS (the Experiences in Close Relationships-Revised Short).Additionally, the terms and abbreviations used in the presentation are defined as follows: Q1 (first quartile), Q3 (third quartile), Md (median), Q1 (first quartile, 25%), Q3 (third quartile, 75%).